{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T22:47:25Z","timestamp":1776898045102,"version":"3.51.2"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T00:00:00Z","timestamp":1721606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T00:00:00Z","timestamp":1721606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s13042-024-02272-7","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T09:02:44Z","timestamp":1721638964000},"page":"5667-5681","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep feature dendrite with weak mapping for small-sample hyperspectral image classification"],"prefix":"10.1007","volume":"15","author":[{"given":"Gang","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jiaying","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoyuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yajing","family":"Pang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,22]]},"reference":[{"key":"2272_CR1","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","volume":"15","author":"M Ahmad","year":"2021","unstructured":"Ahmad M, Shabbir S, Roy SK, Hong D, Wu X, Yao J, Khan AM, Mazzara M, Distefano S, Chanussot J (2021) Hyperspectral image classification\u2014Traditional to deep models: a survey for future prospects. IEEE J Select Top Appl Earth Observ Remote Sens 15:968\u2013999","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"key":"2272_CR2","doi-asserted-by":"crossref","unstructured":"Alem A, Kumar S (2020) Deep learning methods for land cover and land use classification in remote sensing: a review. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO)","DOI":"10.1109\/ICRITO48877.2020.9197824"},{"issue":"8","key":"2272_CR3","doi-asserted-by":"crossref","first-page":"6391","DOI":"10.1007\/s10462-021-09975-1","volume":"54","author":"MM Bejani","year":"2021","unstructured":"Bejani MM, Ghatee M (2021) A systematic review on overfitting control in shallow and deep neural networks. Artif Intell Rev 54(8):6391\u20136438","journal-title":"Artif Intell Rev"},{"issue":"7","key":"2272_CR4","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.1109\/TGRS.2020.2964627","volume":"58","author":"X Cao","year":"2020","unstructured":"Cao X, Yao J, Xu Z, Meng D (2020) Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans Geosci Remote Sens 58(7):4604\u20134616","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"2272_CR5","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"Y Chen","year":"2016","unstructured":"Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232\u20136251","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"2272_CR6","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","volume":"7","author":"Y Chen","year":"2014","unstructured":"Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Select Top Appl Earth Observ Remote Sens 7(6):2094\u20132107","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"issue":"6","key":"2272_CR7","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","volume":"8","author":"Y Chen","year":"2015","unstructured":"Chen Y, Zhao X, Jia X (2015) Spectral\u2013spatial classification of hyperspectral data based on deep belief network. IEEE J Select Top Appl Earth Observ Remote Sens 8(6):2381\u20132392","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"issue":"3","key":"2272_CR8","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1145\/212094.212114","volume":"27","author":"T Dietterich","year":"1995","unstructured":"Dietterich T (1995) Overfitting and undercomputing in machine learning. ACM Comput Surv (CSUR) 27(3):326\u2013327","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"3","key":"2272_CR9","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TGRS.2018.2865953","volume":"57","author":"L Fang","year":"2018","unstructured":"Fang L, Liu G, Li S, Ghamisi P, Benediktsson JA (2018) Hyperspectral image classification with squeeze multibias network. IEEE Trans Geosci Remote Sens 57(3):1291\u20131301","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"21","key":"2272_CR10","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.3390\/rs13214407","volume":"13","author":"Y Feng","year":"2021","unstructured":"Feng Y, Zheng J, Qin M, Bai C, Zhang J (2021) 3D octave and 2D vanilla mixed convolutional neural network for hyperspectral image classification with limited samples. Remote Sens 13(21):4407","journal-title":"Remote Sens"},{"key":"2272_CR11","unstructured":"Gang L. It may be time to improve the neuron of artificial neural network. TechRxiv. Preprint."},{"key":"2272_CR12","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1109\/JSTARS.2020.3002787","volume":"13","author":"K Gao","year":"2020","unstructured":"Gao K, Guo W, Yu X, Liu B, Yu A, Wei X (2020) Deep induction network for small samples classification of hyperspectral images. IEEE J Select Top Appl Earth Observ Remote Sens 13:3462\u20133477","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"key":"2272_CR13","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1109\/TIP.2022.3169689","volume":"31","author":"K Gao","year":"2022","unstructured":"Gao K, Liu B, Yu X, Yu A (2022) Unsupervised meta learning with multiview constraints for hyperspectral image small sample set classification. IEEE Trans Image Process 31:3449\u20133462","journal-title":"IEEE Trans Image Process"},{"key":"2272_CR14","doi-asserted-by":"crossref","first-page":"7570","DOI":"10.1109\/JSTARS.2021.3099118","volume":"14","author":"S Ghaderizadeh","year":"2021","unstructured":"Ghaderizadeh S, Abbasi-Moghadam D, Sharifi A, Zhao N, Tariq A (2021) Hyperspectral image classification using a hybrid 3D\u20132D convolutional neural networks. IEEE J Select Top Appl Earth Observ Remote Sens 14:7570\u20137588","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"issue":"3","key":"2272_CR15","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/JMASS.2020.3019669","volume":"1","author":"JM Haut","year":"2020","unstructured":"Haut JM, Paoletti ME (2020) Cloud implementation of multinomial logistic regression for UAV hyperspectral images. IEEE J Miniatur Air Space Syst 1(3):163\u2013171","journal-title":"IEEE J Miniatur Air Space Syst"},{"key":"2272_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"2272_CR17","first-page":"1","volume":"60","author":"D Hong","year":"2021","unstructured":"Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, Chanussot J (2021) SpectralFormer: rethinking hyperspectral image classification with transformers. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR18","doi-asserted-by":"crossref","unstructured":"Hu L, He W, Zhang L, Zhang H (2023) Cross-domain meta-learning under dual adjustment mode for few-shot hyperspectral image classification. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2023.3320657"},{"key":"2272_CR19","doi-asserted-by":"crossref","unstructured":"Hu L, Luo X, Wei Y (2020) Hyperspectral image classification of convolutional neural network combined with valuable samples. J Phys Conf Ser","DOI":"10.1088\/1742-6596\/1549\/5\/052011"},{"key":"2272_CR20","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.geoderma.2016.01.018","volume":"268","author":"S Jia","year":"2016","unstructured":"Jia S, Li H, Wang Y, Tong R, Li Q (2016) Recursive variable selection to update near-infrared spectroscopy model for the determination of soil nitrogen and organic carbon. Geoderma 268:92\u201399","journal-title":"Geoderma"},{"issue":"3","key":"2272_CR21","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","volume":"50","author":"J Li","year":"2011","unstructured":"Li J, Bioucas-Dias JM, Plaza A (2011) Spectral\u2013spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans Geosci Remote Sens 50(3):809\u2013823","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"2272_CR22","doi-asserted-by":"crossref","first-page":"3136","DOI":"10.1109\/TGRS.2019.2948865","volume":"58","author":"R Li","year":"2019","unstructured":"Li R, Pan Z, Wang Y, Wang P (2019) A convolutional neural network with mapping layers for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58(5):3136\u20133147","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"9","key":"2272_CR23","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","volume":"57","author":"S Li","year":"2019","unstructured":"Li S, Song W, Fang L, Chen Y, Ghamisi P, Benediktsson JA (2019) Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens 57(9):6690\u20136709","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"2272_CR24","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/05704928.2021.1999252","volume":"58","author":"X Li","year":"2023","unstructured":"Li X, Li Z, Qiu H, Hou G, Fan P (2023) An overview of hyperspectral image feature extraction, classification methods and the methods based on small samples. Appl Spectrosc Rev 58(6):367\u2013400","journal-title":"Appl Spectrosc Rev"},{"key":"2272_CR25","doi-asserted-by":"crossref","unstructured":"Li Z, Guo H, Chen Y, Liu C, Du Q, Fang Z (2023) Few-shot hyperspectral image classification with self-supervised learning. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2023.3298851"},{"issue":"3","key":"2272_CR26","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2011.2172185","volume":"9","author":"G Licciardi","year":"2011","unstructured":"Licciardi G, Marpu PR, Chanussot J, Benediktsson JA (2011) Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geosci Remote Sens Lett 9(3):447\u2013451","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"12","key":"2272_CR27","doi-asserted-by":"crossref","first-page":"13774","DOI":"10.1109\/TCYB.2021.3124328","volume":"52","author":"G Liu","year":"2021","unstructured":"Liu G, Wang J (2021) Dendrite net: a white-box module for classification, regression, and system identification. IEEE Trans Cybern 52(12):13774\u201313787","journal-title":"IEEE Trans Cybern"},{"key":"2272_CR28","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/TNSRE.2022.3149654","volume":"30","author":"G Liu","year":"2022","unstructured":"Liu G, Wang J (2022) EEGG: an analytic brain-computer interface algorithm. IEEE Trans Neural Syst Rehabil Eng 30:643\u2013655","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"2272_CR29","first-page":"1","volume":"61","author":"Q Liu","year":"2023","unstructured":"Liu Q, Peng J, Ning Y, Chen N, Sun W, Du Q, Zhou Y (2023) Refined prototypical contrastive learning for few-shot hyperspectral image classification. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"2272_CR30","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.1109\/TGRS.2020.3018879","volume":"59","author":"S Liu","year":"2020","unstructured":"Liu S, Shi Q, Zhang L (2020) Few-shot hyperspectral image classification with unknown classes using multitask deep learning. IEEE Trans Geosci Remote Sens 59(6):5085\u20135102","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"2272_CR31","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","volume":"42","author":"F Melgani","year":"2004","unstructured":"Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778\u20131790","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR32","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.geoderma.2016.11.030","volume":"289","author":"W Ng","year":"2017","unstructured":"Ng W, Malone BP, Minasny B (2017) Rapid assessment of petroleum-contaminated soils with infrared spectroscopy. Geoderma 289:150\u2013160","journal-title":"Geoderma"},{"issue":"2","key":"2272_CR33","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","volume":"57","author":"ME Paoletti","year":"2018","unstructured":"Paoletti ME, Haut JM, Fernandez-Beltran R, Plaza J, Plaza AJ, Pla F (2018) Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(2):740\u2013754","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"2272_CR34","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","volume":"5","author":"S Prasad","year":"2008","unstructured":"Prasad S, Bruce LM (2008) Limitations of principal components analysis for hyperspectral target recognition. IEEE Geosci Remote Sens Lett 5(4):625\u2013629","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"9","key":"2272_CR35","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352\u20132449","journal-title":"Neural Comput"},{"issue":"2","key":"2272_CR36","first-page":"277","volume":"17","author":"SK Roy","year":"2019","unstructured":"Roy SK, Krishna G, Dubey SR, Chaudhuri BB (2019) HybridSN: exploring 3-D\u20132-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci Remote Sens Lett 17(2):277\u2013281","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"7","key":"2272_CR37","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1109\/TGRS.2008.916629","volume":"46","author":"L Samaniego","year":"2008","unstructured":"Samaniego L, B\u00e1rdossy A, Schulz K (2008) Supervised classification of remotely sensed imagery using a modified $ k $-NN technique. IEEE Trans Geosci Remote Sens 46(7):2112\u20132125","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR38","first-page":"1","volume":"60","author":"L Sun","year":"2022","unstructured":"Sun L, Zhao G, Zheng Y, Wu Z (2022) Spectral\u2013spatial feature tokenization transformer for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR39","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/JSTARS.2021.3133009","volume":"15","author":"B Tu","year":"2021","unstructured":"Tu B, He W, He W, Ou X, Plaza A (2021) Hyperspectral classification via global-local hierarchical weighting fusion network. IEEE J Select Top Appl Earth Observ Remote Sens 15:184\u2013200","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"issue":"12","key":"2272_CR40","doi-asserted-by":"crossref","first-page":"4865","DOI":"10.1109\/TGRS.2011.2153861","volume":"49","author":"A Villa","year":"2011","unstructured":"Villa A, Benediktsson JA, Chanussot J, Jutten C (2011) Hyperspectral image classification with independent component discriminant analysis. IEEE Trans Geosci Remote Sens 49(12):4865\u20134876","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"2272_CR41","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.3390\/rs10071068","volume":"10","author":"W Wang","year":"2018","unstructured":"Wang W, Dou S, Jiang Z, Sun L (2018) A fast dense spectral\u2013spatial convolution network framework for hyperspectral images classification. Remote Sens 10(7):1068","journal-title":"Remote Sens"},{"issue":"2","key":"2272_CR42","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","volume":"56","author":"X Xu","year":"2017","unstructured":"Xu X, Li W, Ran Q, Du Q, Gao L, Zhang B (2017) Multisource remote sensing data classification based on convolutional neural network. IEEE Trans Geosci Remote Sens 56(2):937\u2013949","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR43","first-page":"1","volume":"60","author":"Z Xue","year":"2022","unstructured":"Xue Z, Liu B, Yu A, Yu X, Zhang P, Tan X (2022) Self-supervised feature representation and few-shot land cover classification of multimodal remote sensing images. IEEE Trans Geosci Remote Sens 60:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR44","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/TIP.2023.3244414","volume":"32","author":"J Yang","year":"2023","unstructured":"Yang J, Du B, Xu Y, Zhang L (2023) Can spectral information work while extracting spatial distribution?\u2014An online spectral information compensation network for HSI classification. IEEE Trans Image Process 32:2360\u20132373","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"2272_CR45","doi-asserted-by":"crossref","first-page":"12745","DOI":"10.1109\/TCYB.2021.3088519","volume":"52","author":"Q Ye","year":"2021","unstructured":"Ye Q, Huang P, Zhang Z, Zheng Y, Fu L, Yang W (2021) Multiview learning with robust double-sided twin SVM. IEEE Trans Cybern 52(12):12745\u201312758","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"2272_CR46","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TCSVT.2016.2596158","volume":"28","author":"Q Ye","year":"2016","unstructured":"Ye Q, Yang J, Liu F, Zhao C, Ye N, Yin T (2016) L1-norm distance linear discriminant analysis based on an effective iterative algorithm. IEEE Trans Circuits Syst Video Technol 28(1):114\u2013129","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2272_CR47","first-page":"1","volume":"61","author":"Z Ye","year":"2023","unstructured":"Ye Z, Wang J, Liu H, Zhang Y, Li W (2023) Adaptive domain-adversarial few-shot learning for cross-domain hyperspectral image classification. IEEE Trans Geosci Remote Sens 61:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2272_CR48","doi-asserted-by":"crossref","unstructured":"Ying X (2019) An overview of overfitting and its solutions. J Phys Conf Ser","DOI":"10.1088\/1742-6596\/1168\/2\/022022"},{"key":"2272_CR49","first-page":"1","volume":"61","author":"J Zeng","year":"2023","unstructured":"Zeng J, Xue Z, Zhang L, Lan Q, Zhang M (2023) Multistage relation network with dual-metric for few-shot hyperspectral image classification. IEEE Trans Geosci Remote Sens 61:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"2272_CR50","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/TGRS.2020.2995575","volume":"59","author":"J Zheng","year":"2020","unstructured":"Zheng J, Feng Y, Bai C, Zhang J (2020) Hyperspectral image classification using mixed convolutions and covariance pooling. IEEE Trans Geosci Remote Sens 59(1):522\u2013534","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"2272_CR51","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","volume":"56","author":"Z Zhong","year":"2017","unstructured":"Zhong Z, Li J, Luo Z, Chapman M (2017) Spectral\u2013spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans Geosci Remote Sens 56(2):847\u2013858","journal-title":"IEEE Trans Geosci Remote Sens"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02272-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02272-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02272-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T10:25:56Z","timestamp":1730197556000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02272-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,22]]},"references-count":51,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["2272"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02272-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,22]]},"assertion":[{"value":"18 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}